1. Field of the Invention
Modern computers and modern computer networks enable the transfer of a significant amount of information between computers and between a computer and a storage device. When computers access local storage devices, such as a local hard drive or local floppy drive, significant amounts of information can be quickly accessed. However, when seeking to access data from a remote storage location such as over a wide area network (WAN), the internet, or a wireless communication channel (cellular phone network, etc), data transfer rates are significantly slower. Transferring large files, therefore, takes significant amounts of time. Additionally, storage of large files utilizes valuable and limited storage space. Photographic images and similar graphical images typically are considered to be large files, since an image conventionally requires information on each picture element or pixel in the image. Photographs and similar graphical images, therefore, typically require over one megabyte of storage space, and therefore require significant transmission times over slow network communications. In recent years, therefore, numerous protocols and standards have been developed for compressing photographic images to reduce the amount of storage space required to store photographic images, and to reduce transfer and rendering times. The compression methods essentially create mathematical or statistical approximations of the original image.
Compression methods can broadly be categorized into two separate categories: Lossy compression methods are methods wherein there is a certain amount of loss of fidelity of the image; in other words, close inspection of the reproduced image would show a loss of fidelity of the image. Lossless compression methods are ones where the original image is reproduced exactly after decoding. The present invention is directed to an efficient image compression method and apparatus wherein a part, or parts, of an image can be compressed with a higher level of fidelity in the reproduced image than other parts of the image, based on a selection of region-of-interests by the user or the system which is initially encoding or compressing the image, or the user or the system which receives and decodes the image data through interaction with the encoding side.
2. Description of the Related Art
A currently popular standard for compressing images is called the JPEG or “J-peg” standard. This standard was developed by a committee called The Joint Photographic Experts Group, and is popularly used to compress still images for storage or network transmission. Recent papers by Said and Pearlman discuss new image coding and decoding methods based upon set partitioning in hierarchical trees (SPIHT). See Said and Pearlman, Image Codec Based on Set Partitioning in Hierarchical Trees, IEEE Transactions on Circuits and Systems for Video Technology, vol. 6, no. 3, June 1996, and Said and Pearlman, Image Multi-Resolution Representation, IEEE Transactions on Image Processing, vol. 5, no. 9, September 1996. The contents of these papers are hereby incorporated by reference. These references disclose computer software which, when loaded and running on a general purpose computer, performs a method and creates an apparatus which utilizes integer wavelet transforms which provide lossy compression by bit accuracy and lossless compression within a same embedded bit stream, or an apparatus which utilizes non-integer wavelet transforms which provide lossy compression by bit accuracy within a single embedded bit stream. An image which is initially stored as a two dimensional array representing a plurality of individual pixels prioritizes bits according to a transform coefficient for progressive image transmission. The most important information is selected by determining significant or insignificant elements with respect to a given threshold utilizing subset partitioning. The progressive transmission scheme disclosed by Said and Pearlman selects the most important information to be transmitted first based upon the magnitude of each transform coefficient; if the transform is unitary, the larger the magnitude, the more information the coefficient conveys in the mean squared error (MSE, Dmse( )) sense;
            D      mse        ⁡          (              p        -                  p          _          ^                    )        =                                                    p            -                                          p                ^                            _                                                2            N        =                  1        N            ⁢                        ∑          i                ⁢                              ∑            j                    ⁢                                    (                                                p                                      i                    ,                    j                                                  -                                                                            p                      ^                                        _                                                        i                    ,                    j                                                              )                        2                              
where (i,j) is the pixel coordinate, with p, therefore representing a pixel value. Two dimensional array c is coded according to c=Ω(p), with Ω(.) being used to represent a unitary hierarchical subband transformation. Said and Pearlman make the assumption that each pixel coordinate and value is represented according to a fixed-point binary format with a relatively small number of bits which enables the element to be treated as an integer for the purposes of coding. The reconstructed image {circumflex over (p)} is performed by setting a reconstruction vector ĉ to 0, and calculating the image as:{circumflex over (p)}=Ω−1(ĉ)
N is the number of image pixels, and the above calculation for mean squared-error distortion can therefore be made. Using mathematical assumptions, it is known that the mean squared-error distortion measure decreases by ∥Cij∥2/N. This fact enables pixel values to be ranked according to their binary representation, with the most significant bits (MSBs) being transmitted first, and also enables pixel coefficients with larger magnitude to be transmitted first because of a larger content of information. An algorithm is utilized by the encoder to send a value representing the maximum pixel value for a particular pixel coordinate, sorting pixel coordinates by wavelet transform coefficient values, then outputting a most significant bit of the various coefficients, using a number of sorting passes and refinement passes, to provide high quality reconstructed images utilizing a small fraction of the transmitted pixel coordinates. A user can set a desired rate or distortion by setting the number of bits to be spent in sorting passes and refinement passes.